Most machine learning (ML) methods are based on a simple principle: fit a predictor map
to given training data points [1]}, [2]}, [3]}.
This empirical risk minimization paradigm is also driving deep learning methods .
The various ML methods differ merely in their choice for the hypothesis spaces which
collects all feasible predictor maps to choose from and the loss function to measure the
quality of a fit.
